Role of inflammation in determining the severity of COVID-19 infection in patients with diabetes: A comparative study

There is a need to consider the geographical origins when studying the association between COVID-19 and the comorbid conditions. To examine the role of inflammation in determining the severity of COVID-19 among hospitalized patients with diabetes and compare these roles with those who does not have diabetes. A cross sectional comparative design was used with a convenience sample of 352 patients. Samples were collected from hospitalized patients with COVID-19 who were divided into 2 groups (diabetes and non-diabetes). Data regarding results of selected inflammatory markers and sociodemographic were collected. The severity of COVID-19 differed significantly between the diabetes and non-diabetes groups (Chi square = 25.58 P < .05). There was significant difference in the mean scores of neutrophil counts, monocyte count, Basophil count, erythrocyte sedimentation rate, partial thromboplastin time, C-creative protein, platelets, white blood cells, and mean cellular hemoglobin center between those with and those without diabetes. The diabetes were shown more increased in the predictors and severity of the COVID-19 disease. However, neutrophil to lymphocyte ratio, neutrophil count, and age were the significant predictors of the severity level of COVID-19 among patients with diabetes. In conclusion, our study addressed the influence of having diabetes among hospitalized patients with moderate and severe COVID-19 infection. The results showed that severity of COVID-19 infection was affected by diabetes where those with diabetes had more tendency to suffer from the severe form of the disease rather that the moderate level.


Introduction
[3] COVID-19 has affected over 100 million people worldwide and caused over 6 million deaths so far, and over 700 thousand confirmed new cases are reported daily. [4]In Jordan, the total number of cases so far exceeded 1.7 million and the fatalities so far had surpassed 14 thousand.The report on admission showed that about 159 cases are admitted per week. [5][8] The relationship between diabetes and COVID-19 infection was discussed by Gangadaran et al [9] in their review paper where they indicated that diabetes is linked [10] to the severity of the COVID-19 and its rapid progression.The negative impact of diabetes on the health outcomes of patients with COVID-19 was attributed to hyperglycemic changes and immune responses.In their article, the authors argued that hyperglycemia leads to metabolic changes and significant increase in the monocytes.These processes can upregulate the proteins involved in cell damage and stimulate several immune cells which causes elevation in some inflammatory cytokines such as tumor necrosis factor α (TNF-α), interleukin 1 beta, and interleukin 6 proteins (IL-6).12] Varikasuvu et al [13] conducted a systematic review study to analyze and evaluate studies in the literature that examined the changes in inflammatory and coagulation markers in The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

Gharaibeh et al. • Medicine (2023) 102:50
Medicine patients with diabetes.In their review paper the authors stated that despite the plentiful of studies addressed inflammation in patients with diabetes and COVID-19-19, much attention must be focused on comparing diabetes versus non-diabetes COVID-19 patients, and further studies are needed in future to explain the relationships between inflammation, diabetes, and COVID-19 infection.Moreover, Bradley et al [14] in their meta-analysis study reported that despite the overwhelming number of studies that concluded effect of diabetes on COVID-19 mortality, there is evidence that showed this effect is mediated by different factors and the difference in COVID-19 health outcomes was not different between diabetes and non-diabetes patients in some cases.Also, the authors stated that there were inconsistencies and variations in the definitions of the outcomes addressed in the reviewed studies, thus, the results of the many studies should be interpreted in their study populations.So, studies should consider diverse cohorts specifically individuals hospitalized for COVID-19 infection. [15]oreover, when addressing the role of diabetes in COVID-19 infection, there is a need to factor in the geographical origins because there is variability across the world regions which may significantly skew overall trends. [16]In addition to healthrelated problems, the pandemic has also become the interest in the research studies, several studies have shown increased pro-inflammatory cytokines in serum of COVID-19 patients.However, the role of inflammatory markers among diabetic patients and none in monitoring the severity of COVID-19 is still unclear. [17]he purpose of our study is to examine the role of inflammation in determining the severity of COVID-19 among hospitalized patients with diabetes.Studying these relationships can provide better scientific knowledge about the mechanisms by which the immune system and the inflammatory process are affected during COVID-19 infection, which may assist in generating strategies and protocols that can help in treating the infection and improving the health outcomes for patients with COVID-19-19.
Research questions are: • Are there differences in the level of COVID-19 severity between diabetes and non-diabetes groups?• Are there differences in the mean scores of the inflammatory markers between diabetes and non-diabetes groups?• Are there differences in the mean scores of the inflammatory markers between the 2 level of COVID-19 severity (moderate and severe) groups?• What are the inflammatory markers that significantly influence the level of COVID-19 severity among diabetes and no-diabetes groups?

Design
A cross-sectional design was used to address the severity of COVID-19 in patients with and patients without diabetes.The patient record was accessed and used to determine whether the patient has or does not have diabetes based on the medical diagnosis.

Sample
A convenience sampling technique was used to recruit participants from the targeted hospitals.The inclusion criteria were: (1) patient have confirmed diagnosis of moderate or severe COVID-19-19; (2) equal or more than 18 years of age; and (3) can speak and read Arabic language.The exclusion criteria were those diagnosed as critical cases that related to respiratory or cardiac system or intubated patients.Also, people with chronic diseases related to cardiac system were excluded.The Jordanian Ministry of Health protocol (2022) stated that the confirmed COVID-19 case is defined as the case that is laboratoryconfirmed by a PCR examination through a positive result to detect the SARS-CoV2 virus.The protocol stated that cases are diagnosed in Jordan only by adopting the polymerase chain reaction test.Test samples are taken using nasopharyngeal swab, sputum sample, or pulmonary lysing sample.Two or more samples can be taken depending on the availability of the samples and according to the opinion of the attending physician, as required by the patient's condition.
The COVID-19 infection in Jordan is diagnosed based on the results of Real-Time PCR (RT-PCR) test (COVID-19 MDx RT-PCR COVID-19 DETECTION KITS).Those tests are FDA approved by Jordanian Food and Drug Administration.The RT-PCR is an in vitro diagnostic that is used to detect SARS-CoV-2 in nasopharyngeal/oropharyngeal swabs, anterior/ mid-turbinate nasal swabs.The estimated sensitivity of the test was reported around 80% and specificity about 99%. [18]he minimum sample size was estimated using G*Power 3.1 for ANOVA test.The following parameters were used to estimate the sample size: alpha = .05,beta = .80,and effect size of 0.07 (representing small to medium effect size).An estimated dropout percentage of 15% was considered.Thus, the minimum final estimated sample size was 350 participants.The final recruited sample was 352 participants.These participants were all their biomarkers test were done with hospitals.The sample size was calculated to determine the required number to answer the questions of the paper.

Sociodemographic and potential confounders
A sociodemographic sheet was used to collect data regarding various demographic characteristics such as age, gender, marital status, and other characteristics.Also, questions to collect data regarding potential confounders such as comorbid conditions and receiving COVID-19 vaccine was included in this sheet.These data were confirmed by returning to the patients' charts.

Severity of COVID-19 infection
The severity of COVID-19 infection was measured as an ordinal variable to determine whether the patient is considered to have moderate or severe COVID-19 19 infections.The protocol that was proposed by the diagnostic and treatment protocol for patients with the emerging coronavirus (COVID- 19-19) issued by the Jordanian Ministry of Health and approved by the National Committee for Epidemic Control; was used to describe if the case is considered moderate or severe COVID-19 infection as previously mentioned.Jordan protocol indicates that the patient who suffers from symptoms and signs of lower respiratory tract infections (bronchitis or pneumonia), including shortness of breath, and the percentage of hemoglobin saturation with oxygen is more than 94% is considered as a moderate COVID-19 infection.On the other hand, the severe case is a patient who suffers from pulmonary infections with shortness of breath, and the percentage of hemoglobin saturation with oxygen is <94%.However, the X-rays and CT scans were considered to improve the severity of the cases.

Inflammatory biomarkers
The results of the inflammatory biomarkers for the corresponding participants were gathered from the hospital records.The protocol by the MOH described the required tests for all admitted/hospitalized patients with COVID-19 19 infection; this protocol indicated that the following tests should be done for the hospitalized COVID-19 19 patients on daily basis.

Settings
Three of the hospitals that were allocated by the Jordanian government as hospitals to receive patients with COVID-19 19 infection were selected for data collection.Only certain hospitals were selected by the government to admit COVID- 19 19 patients.Other hospitals were instructed to transfer any COVID-19 patients to those previously identified as precaution to control disease transmission.

Data collection procedure
Data collection was done between May 1st, 2022, and June 4th, 2022.After acquiring the IRB and the approval from the selected hospitals, the investigator introduced the topic to the head nurse and staff nurses to provide clarification regarding the study and the questionnaire.The investigator distributed the demographic questionnaire with other related questions to the potential participants who met the inclusion criteria that previously described.Those who agreed to participate signed an informed consent and completed the questionnaire that related demographic and other important information that listed before.The investigator addressed any concerns and questions from the potential participants and checked the questionnaire to ensure there was no missing data.If missing data were noticed, the participants were instructed to fill that part appropriately.Results of the inflammatory markers were collected from the corresponding participant's records by the investigator and were recorded on printed tables.

Data analysis procedures
The SPSS program version 25 (IBM Corp, Armonk, RRID:SCR_016479) was used to analyze the data.The study variables were explained using descriptive statistics (e.g., frequency, percentage, mean, standard deviation, and range).Using the receiver operating characteristic (ROC) and area under the ROC curve (AUC) analysis, each marker was plotted against COVID severity with the value of state variable set as (2) indicating severe COVID infection.Logistic regression to determine the predictors, Chi square to determine the difference between DM and non-DM participants, and ANOVA tests was used to determine the difference between different degree of DM patients.P-value for our study was determined at level of (.05).

Ethical consideration
This study approved by Jordan University of Science and Technology (IRB number 656-2021) (Jordan University of Science and Technology).The authors were explained all the risks and benefits for the participants.All participants were signed consent form for participation in this study.The written informed consent were received from all the participants.

Are there differences in the level of COVID-19 severity between diabetes and non-diabetes groups?
To test for presence of significant difference in these groups, chi square test was conducted.The results showed that the Pearson Chi square was (Chi square = 25.58P < .05).The descriptive tables showed that a total of 274 patients had severe COVID-19 which 166 had diabetes and 108 did not have diabetes.Meanwhile, only 22 patients with diabetes had moderate COVID-19 whereas 56 patients with no diabetes had moderate COVID-19 infection.
Further analyses were conducted to test the relationship between the inflammatory markers and the severity of COVID-19 infection.These analyses were conducted to determine the accuracy of the individual classification models representing the relationship between the inflammatory marker of interest with the severity of COVID infection.Using the ROC and AUC analysis, each marker was plotted against COVID severity with the value of state variable set as (2) indicating severe COVID infection.For patients with no diabetes, the results of the analyses showed that most of the markers of interest had an AUC above the set level of (0.5).D-DIMER was the marker with the highest AUC among all the established models.Moreover, for patients with diabetes, the analyses showed thatIL-6 had the model with highest AUC.Age was also included in the analyses and was established as an important factor that contributed to the severity of COVID infection.See Figures 1 and 2 for the ROC curves in patients with diabetes, and patients with no diabetes.See Table 2 and 3. 3.3.Are there differences in the mean scores of the inflammatory markers between diabetes and non-diabetes groups?
To answer this research question, ANOVA test was conducted.
The results showed that the mean scores of neutrophil counts, monocyte count, Basophil count, ESR, PTT, CRP, platelets, white blood cells, and MCHC differed significantly between those with and those without diabetes.See Table 4 for the ANOVA test with having or not having diabetes as grouping variable.
3.4.Are there differences in the mean scores of the inflammatory markers between the 2 level of COVID-19 severity (moderate and severe) groups The mean scores of the monocyte count, BNB, CRP, D-Dimer, partial thrombin, PTT, IL-6, ESR, platelets, and mean corpuscular hemoglobin differed significantly between those with moderate COVID-19 severity and those with severe COVID-19 infection.See Table 5 to see the results of the ANOVA test with COVID-19 severity as grouping variable.

What are the inflammatory markers that significantly influence the level of COVID-19 severity among diabetes and no-diabetes groups?
To answer this research question, 2 logistic regression tests were conducted.In these 2 logistic tests, the same markers and variables were entered to the equations.

Table 2
Area under the curve for the models representing the inflammatory markers to determine their effect on the severity of COVID-19 in patients with no diabetes.

(MCHB)
.  that approximately half the variability of the severity of COVID-19 was predicted/explained by the model.See Table 6 for the variables that predicted the severity and the corresponding odd ratios.

Discussion
Our study aimed at examining the role of inflammation in determining the severity of COVID-19 among hospitalized patients with diabetes.So, various statistical analyses were done to assess the associations between the study variables; more specifically, to assess whether diabetes played a role in determining the type and strength of the relationships between inflammation and the severity of COVID-19 infection.Our results showed that there was significant difference in term of COVID-19 severity between patient with and patients without diabetes.This finding is congruent with the literature were most of the studies that addressed this issue concluded the same finding. [16,19]These findings also support the current evidence regarding the increased mortality in COVID-19 among patients with diabetes.For example, Harbuwono et al [20] found that COVID-19 mortality among his study sample was 2 folds higher in patients with diabetes compared to those with no diabetes even after accounting for other comorbid conditions and accounting for diabetes complications.To explain the high mortality, several studies pointed out the possible role of inflammation specially that diabetes in known to affect the immune system.For example, Guo et al, [21] indicated that dysregulated innate immune response is considered an important factor in explaining COVID-19 mortality because inflammation plays a major role in disease severity; however, these relationships are complex.This complexity was proposed because contradictory results were reported regarding the levels of the inflammatory markers and because the contradictory findings regarding the efficiency of cytokine inhibitors.In another study, study investigating the role of D-dimer test in identifying the severity of COVID-19 pandemic, Yao et al [22] utilized a retrospective study design to analyze characteristics of COVID-19 illness among 248 patients.
In our study, the authors found that, among many other studied factors, the increase of D-dimer was a sole variable to increase the odds of mortality among COVID-19 patients.Thrombosis, pulmonary embolism, and deep venous thrombosis were associated with the mortality of patients.Our study concluded that D-dimer is a reliable biomarker for COVID-19 mortality and is correlated with disease severity.Similarly, In a literature review study that intended to identify the Biomarkers associated with COVID-19 disease progress, Ponti et al [23] found that a multiplicity of biomarkers are associated with the disease including: (1) hematological (lymphocyte count); ( 2 8) aspartate aminotransferase, especially those related to coagulation cascades in disseminated intravascular coagulation and acute respiratory distress syndrome.In previous study higher levels of D-dimer were associated with worse prognosis of COVID-19 patients. [24]Increase of D-dimer and fibrinogen levels by 3 or 4 times during early stages of COVID-19 was associated with higher severity.Previous studies concluded that D-dimer test is a reliable predictor of severity in COVID-19 patients and disease prognosis. [17,24]Also, another study concluded that D-dimer is a reliable biomarker for COVID-19 mortality, and is correlated with disease severity. [22]ur study showed that various inflammatory markers significantly influenced the severity of COVID-19 infection.Our results showed that the mean scores of neutrophil counts, monocyte count, Basophil count, ESR, PTT, CRP, platelets, white blood cells, and MCHC differed significantly between those with and those without diabetes However, the inflammatory markers that significantly affected the severity of COVID-19 among patients with diabetes were not the same ones that affected the severity in patients without diabetes.In our study, the only factor that consistently affected the severity was age.This further adds to the complexity of the topic as multiple variables seem to modulate the role of inflammation on the severity of COVID-19 and the associated health outcomes such as the level of mortality.These findings are not dissimilar from the literature as age was consistently addressed as a major influencing factor whereas the role of the inflammation was debatable. [16,21]n our study, interrelations that were emerging through the results of the statistical tests between the inflammatory markers and diabetes, and the inflammatory markers and the severity of COVID-19 were multifaceted.Among the various markers that were tested, only ESR, PTT, and CRP had mean scores that significantly differed between patients with diabetes and patients with no diabetes; and patients with severe and patients with moderate COVID-19 infection.This finding highlights the role these markers and their corresponding inflammatory processes play in determining the severity of COVID-19 infection, and the potential effect they exert in determining the health outcomes of patients with COVID-19-19.Our findings regarding these interrelations are congruent with the findings in the literature [25][26][27] who reported in their review papers that inflammation and hypercoagulation states are observed and correlated to both diabetes and severe COVID-19.However, the etiopathogenetic mechanisms underlying these relationships are not very clear.

Limitations
The findings of our study should be considered within the context of its sample and methodology.The participants were recruited using a non-probability sampling technique; a convenience sampling method, which inherently contains limitations regarding the validity of the findings and its  generalizability.Also, the data was collected from the participants at a single data point.These techniques may produce biased data, and thus may limit the representation of the studies population.Along the same line, the inflammatory markers were only measured during participants' hospitalization without performing follow-up data collection or account for the psychological factors (e.g., anxiety) that may influence the inflammation.These limitations could limit the generalizability of the findings.Therefore, the authors recommend conducting future longitudinal studies with data collection from randomly selected participants to overcome these limitations.

Conclusion
In conclusion, our study addressed the influence of having diaamong hospitalized patients with moderate and severe COVID-19 infection.The results showed that severity of COVID-19 infection was affected by diabetes where those with diabetes had more tendency to suffer from the severe form of the disease rather that the moderate level.The severity of the disease may have been affected by the inflammatory process in the study sample.Some of the inflammatory markers were not significantly different in their levels, while others were significantly different between patients with moderate and patients with severe COVID-19 infection.
First, binary logistic regression to determine the inflammatory markers that signifipredicted the likelihood of the patient to have moderate or severe COVID-19 infection among patients with no diabetes.The results of the analysis showed that (for patients with no diabetes) lymphocyte count, BNB, PCT, age, gender, and whether received COVID-19 vaccine.The test demonstrated the significance of the model (with no diabetes) (model chi square = 77.42;df = 23; P < .05)and Nagelkerke R squared = 0.52 indicating that about half the variability of the severity of COVID-19 was predicted/explained by the model.Then, a second logistic regression was done to assess the relationship between the inflammatory markers and the severity of COVID-19 in patients with diabetes.This analysis showed that a different set of inflammatory markers were significantly associated with the severity of COVID-19 in patients with diabetes.These inflammatory markers included NLR, neutrophil count (absolute count), and age.The test demonstrated the significance of the model (with diabetes) (model chi square = 52.4;df = 23; P < .05)and Nagelkerke R squared = 0.47 indicating

Figure 1 .
Figure 1.Receiver operating characteristic curve (ROC curve) for the main influencing factors (i.e., age, IL-6, and D-DIMER) model plotted against the severity of COVID infection in patients with no diabetes.

Figure 2 .
Figure 2. Receiver operating characteristic curve (ROC curve) for the main influencing factors (i.e., age, IL-6, and D-DIMER) model plotted against the severity of COVID infection in patients with diabetes.

Table 1
sociodemographic characteristics of the study sample.

Table 3
Area under the curve for the models representing the inflammatory markers to determine their effect on the severity of COVID-19 in patients with diabetes.

Table 4
ANOVA test results to assess for significant difference in the mean scores of the tested inflammatory markers between patients with and patients without diabetes.

Table 5
logistic regression to determine factors that influence COVID severity among patients with and those without diabetes.

Table 6
ANOVA test results to assess for significant difference in the mean scores of the tested inflammatory markers between patients with moderate and severe COVID-19 infection.